Postgraduate Course: Health Data Science (GLHE11086)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Data science is revolutionising how medicine is understood, how biomedical research is conducted and how healthcare is delivered. Despite the widely-recognised opportunities that data can bring to biomedicine and healthcare, there is a shortage of data skills in the healthcare sector. This course aims to equip healthcare professionals with the key foundations and data skills that are needed for data-driven innovation. It provides an introduction to key concepts, principles and methods of data science in health, enabling students to explore the potential for data to transform healthcare. Students will learn how to use current data science tools to process healthcare data for effective analysis and reporting, and gain practical experience in working with data. They will also gain critical understandings of ethical and legal implications of working with healthcare data. |
| Course description |
The course aims to provide a broad introduction to data science in health, covering key concepts and principles, data analysis skills and implications of working with healthcare data. Key topics in the course include: types of health data; computational methods (e.g. process modelling and machine learning); data wrangling, analysis and reporting using the R programming language; legal considerations and bias in health data. This online course is based around short recorded videos, which are complemented with readings and discussions in the forums. Hands-on programming tasks in R will equip students with key data skills, and online tutorials will allow students to ask questions and discuss topics of interest.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2021/22, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
| Course Start Date |
09/08/2021 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% Coursework, consisting of:«br /»
- Quizzes (50%)«br /»
- Essay-style assignment (20%)«br /»
- Programming assignment (30%) |
| Feedback |
Not entered |
| No Exam Information |
|
| Academic year 2021/22, Not available to visiting students (SS2)
|
Quota: None |
| Course Start |
Flexible |
| Course Start Date |
11/04/2022 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 5,
Seminar/Tutorial Hours 1,
Online Activities 35,
Feedback/Feedforward Hours 5,
Formative Assessment Hours 5,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
46 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
100% Coursework, consisting of:«br /»
- Quizzes (50%)«br /»
- Essay-style assignment (20%)«br /»
- Programming assignment (30%) |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Explain and critically discuss key concepts, principles and methods of data science in health.
- Apply a range of specialised data science techniques to different medical and healthcare scenarios.
- Analyse health data with the use of the R programming language, including summarisation, visualisation and interpretation.
- Critically examine the ethical, societal and regulatory principles and implications of data science in health.
|
Reading List
There is no compulsory course text. Pointers to appropriate material from different freely-available resources will be
made available online, including the electronic version of the HealthyR textbook. |
Additional Information
| Graduate Attributes and Skills |
By the end of the course, students should have strengthened their skills in:
*Communication, including communicating complex ideas and arguments to a range of audiences with different levels of knowledge/expertise.
*Digital literacy and numeracy, including using advanced data analysis tools to support their research and enquiry.
*Critical and analytical thinking, including applying critical analysis, synthesis and evaluation to key approaches and development in the subject.
*Personal and intellectual autonomy, including planning organising work, time management and taking responsibility for own work. |
| Keywords | Data Science,Healthcare,Health Data,R programming,Ethics,Statistics,Online |
Contacts
| Course organiser | Miss Brittany Blankinship
Tel:
Email: bblankin@exseed.ed.ac.uk |
Course secretary | Miss Magdalena Mazurczak
Tel:
Email: Magdalena.Mazurczak@ed.ac.uk |
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